A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval

Elaine Zosa, Mark Granroth-Wilding, Lidia Pivovarova


Abstract
We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised methods to represent and compare documents: (1) multilingual topic model; (2) cross-lingual document embeddings; and (3) Wasserstein distance.We test the performance of these methods in retrieving news articles in Swedish that are known to be related to a given Finnish article.The results show that ensembles of the methods outperform the stand-alone methods, suggesting that they capture complementary characteristics of the documents
Anthology ID:
2020.clssts-1.6
Volume:
Proceedings of the workshop on Cross-Language Search and Summarization of Text and Speech (CLSSTS2020)
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
CLSSTS | LREC | WS
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
32–37
Language:
English
URL:
https://www.aclweb.org/anthology/2020.clssts-1.6
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.clssts-1.6.pdf